Modern Stats 101

“Possessions are the basic currency of basketball. No matter what the team does with the ball-scores, turns the ball over or misses a shot-the other team gets it when they are done“. — John Hollinger

A game of basketball broken down to its most basic components is decided by who scores the most points before time runs out. Thus the objective of each team is to score more points than the other team. Points scored are determined by how often a team has the ball (a possession), and its ability to convert possessions into points.

That sounds simple enough. To score points (hopefully more than the other guy) you need to have possession of the ball. No possessions, no points.

However, different teams play at different paces. Teams that play fast generate more possessions by shooting early in the shot clock and running at every occasion, while teams that are more deliberate with their pace and shot selection use fewer possessions. But regardless of the pace (speed) that a team plays at, the other team still gets possession of the ball when they are done.

How efficiently a team turns possessions into points determines how efficient the offense is. So in lieu of viewing how a team performs per game, we calculate how a team does per possession. We want to compare apples to apples. It allows us to compare fast teams to slow teams.

So forget about points per game. We’re dealing with possessions. The most common and easy way to deal with these numbers is when they are expressed as points per 100 possessions. How many points a team scores per 100 possessions (which is just slightly more than the average NBA game) is called the team’s offensive rating. Similarly, how many points per 100 possessions a team allows is its defensive rating. These offensive and defensive ratings measure offensive and defensive efficiency, and answer the question “how many points would this team score if it had the ball for 100 possessions”. Over at Basketball-Reference.com they chart offensive and defensive ratings for each team and update daily.

From offensive ratings and defensive ratings, the next thing we want to look at is what are those aspects that contribute to the makeup of those ratings? What causes offenses to be good or bad, and what creates the environment for strong or weak defenses?

Dean Oliver, in Basketball on Paper breaks down offense at the team level into four factors based on statistical analysis, and gives them a weighting based on importance (here in parenthesis). These four are :

Shooting efficiency (10)

Turnover rate (5-6)

Offensive rebounding (4-5)

Free throw conversion. (2-3)

These four factors aren’t equal. Shooting efficiency is twice as important as offensive rebounding, and turnover rate is twice as important as free throw conversion. How well a team does at these four aspects will have a direct correlation on its offensive efficiency.

Shooting efficiency is best viewed by looking at effective field goal percentage (eFG%) rather than the old school field goal percentage (FG%). Effective field goal percentage gives a compensation for the additional point gained by three point conversions. The formula is: eFG% = (FG + 0.5 x 3FG) / FGA

Turnover rate, like points, are measured per possession. It allows us to fairly evaluate the sure-handedness of a team that uses a lot of possessions with that of a team that uses a few. The formula is simply: TO% = turnovers/possessions.

Offensive rebounding is measured by offensive rebounding percentage (OReb%). It measures the percentage of available offensive rebounds a team collects. It allows us to fairly compare the rebounding of teams that shoot poorly (hence there are a lot of offensive rebounds available) with those that shoot well. The formula is: OReb% = offensive rebounds / (offensive rebounds + opponent’s defensive rebounds).

Free throw conversion is expressed as free throw shots made per field goal attempt. The formula is a simple: FTM/FGA.

These four factors also can be viewed from the defensive side of the ball as well (defensive four factors). How well you defend these four areas will coincide with team defensive efficiency. The four factors (both offensively and defensively) are available daily at Basketball-Reference.com and Knickerblogger.net among others.

When talking about player stats instead of team stats, don’t waste your time talking about “per game” numbers. Instead use per minute stats. It lets us compare a player who plays 18 minutes per game to a player who plays 36. Time after time it has been shown that a player’s per minute production stays fairly consistent when minutes are increased or decreased. Again, Basketball-Reference.com and many other sites make per 36 minutes, per 40, even per 48 minute stats available for every player in the NBA all the way back into the 50’s.

Overall player value has been the subject of tens of thousands of written words and endless discussion. There is no “holy grail” tool that fairly and consistently evaluates overall player value. What is out there is a half a dozen or so metrics that basically add up a player’s positive stats and subtract his negative stats. The best of these give a weighting toward the more important statistics and compensate for minutes played. The two that have the most merit and get the broadest use are John Hollinger’s PER (player efficiency rating) and Dave Berri’s Win Score or some variation of it.

Hollinger’s PER is beyond complicated and the average layman would rather have his tongue stapled than attempt to calculate it. It does however compensate for minutes played, it is based on tons of statistical analysis, and can be found on ESPN, Basketball-Reference.com and 82games.com. (The latter also computes a player’s opponent PER.) Hollinger sets the league average PER at 15. The league’s best players have a PER around 30.

Berri’s Win Score is also based on statistical regression analysis. Berri has found out the approximate value of each statistic on team and player wins and losses (how much each rebound contributes to a win for example) and then built a metric which incorporates that data into wins and performance. It has its fans and detractors, but what is nice is that the basic metric is simple enough to calculate quickly. Its step sister compensates for the position each player plays, making comparisons a bit easier still (a point guard would get more assists on average than a center, and a center would likely get more blocks). It’s called the position adjusted win score (PAWS), and you may see it used here from time to time. The league average player at any position is zero. Anything above a zero PAWS is above average; below is below average. The formula is: Pts.+Rebs.+ Steals+ ½ assists +½ block-Fga-t/o-½ Fta-½ Pf/min*48.